Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
Fuzzy regression wiht radial basis function network
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Robust fuzzy regression analysis
Information Sciences: an International Journal
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Interval regression analysis has been a useful tool for dealing with uncertain and imprecise data. Since the available data often contain outliers, robust methods for interval regression analysis are necessary. This paper proposes a genetic-algorithm-based method for determining two functional-link nets for the robust nonlinear interval regression model: one for identifying the upper bound of data interval, and the other for identifying the lower bound of data interval. To facilitate the inclusion of regular data in the robust nonlinear interval regression model, in the fitness function, not only the cost function with different weighting schemes but also the number of training data included in the interval model is taken into account. As for resisting outliers, the effects of training data beyond or beneath the estimated data interval on the determination of upper and lower bounds can be greatly reduced during the training phase when these data are located in the rejection region. Simulation results demonstrate that the proposed method performs well for contaminated data sets by resisting outliers and including all regular data in the data intervals.